Learning Important Features Through Propagating Activation Differences

Proceedings of the 34th International Conference on Machine Learning, PMLR 70:3145-3153, 2017.

Abstract

The purported “black box” nature of neural networks is a barrier to adoption in applications where interpretability is essential. Here we present DeepLIFT (Deep Learning Important FeaTures), a method for decomposing the output prediction of a neural network on a specific input by backpropagating the contributions of all neurons in the network to every feature of the input. DeepLIFT compares the activation of each neuron to its `reference activation’ and assigns contribution scores according to the difference. By optionally giving separate consideration to positive and negative contributions, DeepLIFT can also reveal dependencies which are missed by other approaches. Scores can be computed efficiently in a single backward pass. We apply DeepLIFT to models trained on MNIST and simulated genomic data, and show significant advantages over gradient-based methods. Video tutorial: http://goo.gl/qKb7pL code: http://goo.gl/RM8jvH

Related Material

@InProceedings{pmlr-v70-shrikumar17a,
title = {Learning Important Features Through Propagating Activation Differences},
author = {Avanti Shrikumar and Peyton Greenside and Anshul Kundaje},
booktitle = {Proceedings of the 34th International Conference on Machine Learning},
pages = {3145--3153},
year = {2017},
editor = {Doina Precup and Yee Whye Teh},
volume = {70},
series = {Proceedings of Machine Learning Research},
address = {International Convention Centre, Sydney, Australia},
month = {06--11 Aug},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v70/shrikumar17a/shrikumar17a.pdf},
url = {http://proceedings.mlr.press/v70/shrikumar17a.html},
abstract = {The purported “black box” nature of neural networks is a barrier to adoption in applications where interpretability is essential. Here we present DeepLIFT (Deep Learning Important FeaTures), a method for decomposing the output prediction of a neural network on a specific input by backpropagating the contributions of all neurons in the network to every feature of the input. DeepLIFT compares the activation of each neuron to its `reference activation’ and assigns contribution scores according to the difference. By optionally giving separate consideration to positive and negative contributions, DeepLIFT can also reveal dependencies which are missed by other approaches. Scores can be computed efficiently in a single backward pass. We apply DeepLIFT to models trained on MNIST and simulated genomic data, and show significant advantages over gradient-based methods. Video tutorial: http://goo.gl/qKb7pL code: http://goo.gl/RM8jvH}
}

%0 Conference Paper
%T Learning Important Features Through Propagating Activation Differences
%A Avanti Shrikumar
%A Peyton Greenside
%A Anshul Kundaje
%B Proceedings of the 34th International Conference on Machine Learning
%C Proceedings of Machine Learning Research
%D 2017
%E Doina Precup
%E Yee Whye Teh
%F pmlr-v70-shrikumar17a
%I PMLR
%J Proceedings of Machine Learning Research
%P 3145--3153
%U http://proceedings.mlr.press
%V 70
%W PMLR
%X The purported “black box” nature of neural networks is a barrier to adoption in applications where interpretability is essential. Here we present DeepLIFT (Deep Learning Important FeaTures), a method for decomposing the output prediction of a neural network on a specific input by backpropagating the contributions of all neurons in the network to every feature of the input. DeepLIFT compares the activation of each neuron to its `reference activation’ and assigns contribution scores according to the difference. By optionally giving separate consideration to positive and negative contributions, DeepLIFT can also reveal dependencies which are missed by other approaches. Scores can be computed efficiently in a single backward pass. We apply DeepLIFT to models trained on MNIST and simulated genomic data, and show significant advantages over gradient-based methods. Video tutorial: http://goo.gl/qKb7pL code: http://goo.gl/RM8jvH